Learning under selective labels in the presence of expert consistency
This addresses a pervasive selection bias issue in high-stakes domains like criminal justice and healthcare, but appears incremental as it builds on existing methods for handling selective labels.
The paper tackles the problem of selective labels in algorithm-assisted decision making, such as in predicting recidivism or diagnosing patients, by proposing a data augmentation approach to mitigate partial blindness and validate model reliability, though no concrete numerical results are provided.
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, ranging from predicting recidivism using pre-trial release data to diagnosing patients. In this paper we discuss why selective labels often cannot be effectively tackled by standard methods for adjusting for sample selection bias, even if there are no unobservables. We propose a data augmentation approach that can be used to either leverage expert consistency to mitigate the partial blindness that results from selective labels, or to empirically validate whether learning under such framework may lead to unreliable models prone to systemic discrimination.